Pandas Pipeline for data preprocessing steps

Splitting strategies

The biggest mistake most data scientists make:

They don't use pipelines. Use Sklearn-Pipelines instead of Pandas transformations.

Use Pandas for data analytics. Use Sklearn for ML-models.

Pipelines instantly improve your data transformation process. A pipeline is an independent sequence of steps organized to automate a process. One of the main advantages of using one is the ability to reuse the process at different stages and with different datasets.

Three advantages:

• Cleaner code
• More robust
• Easier to deploy in production

Example of a pipeline → here

At a high level, there are three main steps you need to worry about:

1. Getting the data from its source
2. Processing and cleaning that data
3. Delivering the cleaned data to the right place

Most of the code that goes into training ML models is written either for getting the data to the model or getting the predictions out.

Want fast and reliable models? Spend more time improving your pipelines.